Abstract
The chest computed tomography (CT) is the most commonly used imaging technique for the inspection of lung lesions. In order to provide the physician more valuable preoperative opinions, a powerful computer-aided diagnostic (CAD) system is indispensable. In this paper, we aim to develop an ant colony optimization (ACO-based) classifier to extract the lung mass. We could calculate some information such as its boundary, precise size, localization of tumors, and spatial relations. Final, we reconstructed the extracted lung and tumor regions to a 3D volume module to provide physicians the more reliable vision. In order to validate the proposed system, we have tested our method in a database from 15 lung patients. We also demonstrated the accuracy of the segmentation method using some power statistical protocols. The experiments indicate our method results more satisfied performance in most cases, and can help investigators detect lung lesion for further examination.
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Chen, CJ. (2018). Image Segmentation for Lung Lesions Using Ant Colony Optimization Classifier in Chest CT. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-319-63856-0_35
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DOI: https://doi.org/10.1007/978-3-319-63856-0_35
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